ArticleJournal of the American Medical Informatics Association : JAMIA2016
Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Email-Based Recruitment Into the Health eHeart Study: Cohort Analysis of Invited Eligible Patients.Journal of medical Internet research · 2023Trial
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2024Article
- Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation.BMC musculoskeletal disorders · 2022Review
- Use of a community advisory board to build equitable algorithms for participation in clinical trials: a protocol paper for HoPeNET.BMJ health & care informatics · 2022Article
- Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Artificial intelligence in emergency medicine: A scoping review.Journal of the American College of Emergency Physicians open · 2020Article
- A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department.Pediatric emergency care · 2019Observational
- A Real-Time Automated Patient Screening System for Clinical Trials Eligibility in an Emergency Department: Design and Evaluation.JMIR medical informatics · 2019Article
- Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support.Journal of medical Internet research · 2019Article
- Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis.PloS one · 2018Article
- Precision medicine informatics.Journal of the American Medical Informatics Association : JAMIA · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objective(1) To develop an automated algorithm to predict a patient's response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients' participation response. MATERIALS AND
methodsWe collected 3345 patients' response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients' profiles. Leveraging machine learning methodology, the automated algorithms predicted participation response for individual patients and identified influential features associated with their decision-making. The performance was validated on the collection of actual patient response, where precision, recall, F-measure, and area under the ROC curve were assessed.
resultsCompared to the random response predictor that simulated the current practice, the machine learning algorithms achieved significantly better performance (Precision/Recall/F-measure/area under the ROC curve: 70.82%/92.02%/80.04%/72.78% on 10-fold cross validation and 71.52%/92.68%/80.74%/75.74% on the test set). By analyzing the significant features output by the algorithms, the study confirmed several literature findings and identified challenges that could be mitigated to optimize recruitment.
conclusionBy exploiting predictive variables from multiple sources, we demonstrated that machine learning algorithms have great potential in improving the effectiveness of the recruitment process by automatically predicting patients' participation response to trial invitations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.